A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
By: S. Monira Sumi, M. Faisal Zaman and Hideo Hirose
Open Access
|Dec 2012References
- Abrahart, R.J. and See, L. (2002). Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments,(4): 655-670.
- Baruque, B., Porras, S. and Corchado, E. (2011). Hybrid classification ensemble using topology-preserving clustering,(3): 329-344.
- Chalimourda, A., Scho¨lkopf, B. and Smola, A.J. (2004). Experimentally optimalin support vector regression for different noise models and parameter settings,(1): 127-41.
- Cherkassky, V. and Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression,(1): 113-26.
- Coulibaly, P., Hache´, M., Fortin, V. and Bobe´e, B. (2005). Improving daily reservoir inflow forecasts with model combination,(2): 91.
- Dawson, C.W. and Wilby, R.L. (2001). Hydrological modelling using artificial neural networks,(1): 80-108.
- De Vos, N.J. and Rientjes, T.H.M. (2005). Constraints of artificial neural networks for rainfall-runoff modelling: Trade-offs in hydrological state representation and model evaluation,(1-2): 111-126.
- Deng, Y.-F., Jin, X. and Zhong, Y.-X. (2005). Ensemble SVR for prediction of time series,, Vol. 2, pp. 734-748.
- Diebold, F.X. and Mariano, R.S. (1995). Comparing predictive accuracy,(3): 253-263.
- Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least angle regression,(2): 407-499.
- Everingham, Y.L., Smyth, C.W. and Inman-Bamber, N.G. (2009). Ensemble data mining approaches to forecast regional sugarcane crop production,(3-4): 689-696.
- Fraley, C. and Hesterberg, T. (2009). Least angle regression and LASSO for large datasets,(4): 251-259.
- Fraser, A.M. and Swinney, H.L. (1986). Independent coordinates for strange attractors from mutual information,(2): 1134-1140.
- Friedman, J.H. (1991). Multivariate adaptive regression splines,(1): 1-67.
- Gheyas, I.A. and Smith, L.S. (2011). A novel neural network ensemble architecture for time series forecasting,(18): 3855-3864.
- Hastie, T., Tibshirani, R. and Friedman, J. (2009)., 2nd Edn., Springer, New York, NY.
- Hong, W. (2008). Rainfall forecasting by technological machine learning models,(1): 41-57.
- Hyndman, R.J., Slava R. and Schmidt, D. (2012)., R package version 3.19, http://CRAN.R-project.org/package=forecast
- Kim, T., Heo, J.-H. and Jeong, C.-S. (2006). Multireservoir system optimization in the Han River basin using multi-objective genetic algorithms,(9): 2057-2075.
- Kitanidis, P.K. and Bras, R.L. (1980). Real-time forecasting with a conceptual hydrologic model, 2: Application and results,(6): 1034-1044.
- Lee, C.F., Lee, J.C. and Lee, A.C. (2000).2nd Edn., World Scientific, Singapore.
- Legates, D.R. and McCabe, G.J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation,(1): 233-241.
- Li, P.W. and Lai, E.S.T. (2004). Short-range quantitative precipitation forecasting in Hong Kong,(1-2): 189-209.
- Myers, R.H. (1990)., Duxbury, Boston, MA.
- Nash, J. and Sutcliffe, J. (1970). River flow forecasting through conceptual models, I: A discussion of principles,(3): 282-290.
- Newbold, P., Carlson, W. and Thorne, B. (2007)., 6th Edn., Prentice Hall, Upper Saddle River, NJ.
- Pucheta, J., Patino, D. and Kuchen, B. (2009). A statistically dependent approach for the monthly rainfall forecast from one point observations,D. Li and Z. Chunjiang (Eds.),, IFIP Advances in Information and Communication Technology, Vol. 294, Springer, Boston, MA, pp. 787-798.
- Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation,(1): 39-61.
- Siwek, K., Osowski, S., Szupiluk, R. (2009). Ensemble neural network approach for accurate load forecasting in a power system,(2): 303-315, DOI: 10.2478/v10006-009-0026-2.
- Schölkopf, B. and Smola, A.J. (2002)., Adaptive Computation and Machine Learning, Vol. 98, MIT Press, Cambridge, MA.
- Schoölkopf, B. and Smola, A.J. (2004). A tutorial on support vector regression,(3): 199-122.
- Shrestha, D.L. and Solomatine, D.P. (2006). Machine learning approaches for estimation of prediction interval for the model output,(2): 225-235.
- Solomatine, D.P. and Ostfeld, A. (2008). Data-driven modelling: Some past experiences and new approaches,(1): 3.
- Sudheer, K.P., Gosain, A.K. and Ramasastri, K.S. (2002). A data-driven algorithm for constructing artificial neural network rainfall-runoff models,(6): 1325-1330.
- Syed, A.R. (2011). A review of cross validation and adaptive model selection,, Mathematics Theses, Georgia State University, Arlanta, GA, Paper 99.
- Timmermann, A. (2006). Forecast combinations,G. Elliott, C. Granger and A. Timmermann (Eds.),, Elsevier, Amsterdam, Chapter 4, pp. 135-196.
- Wichard, J. (2011). Forecasting the NN5 time series with hybrid models,(3): 700-707.
- Wichard, J. and Ogorzalek, M. (2007). Time series prediction with ensemble models applied to the CATS benchmark,(13-15): 2371-2378.
- Wu, C., Chau, K. and Li, Y. (2008). River stage prediction based on a distributed support vector regression,(1-2): 96-111.
- Xiong, L., Shamseldin, A. Y. and Oconnor, K. (2001). A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system,(1-4): 196-217.
- Yang, Y., Lin, H., Guo, Z. and Jiang, J. (2007). A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis,(1): 20-30.
- Zaman, M. and Hirose, H. (2011). Classification performance of bagging and boosting type ensemble methods with small training sets,(3): 277-292.
Language: English
Page range: 841 - 854
Published on: Dec 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2012 S. Monira Sumi, M. Faisal Zaman, Hideo Hirose, published by University of Zielona Góra
This work is licensed under the Creative Commons License.